Ministral 8B
Unavailable in this round
Mistral AI
Ministral 8B was Mistral’s compact model class for edge and local applications. The dense checkpoint offers about 8 billion parameters, 128,000 tokens of context, grouped-query attention, and attention optimized for uneven input lengths. Mistral named local translation, robotics, and private assistants; its Research License is not Apache 2.0.
Mistral’s current overview lists the line as deprecated and points to Ministral 3 8B. The released weights remain under the Mistral Research License.
| Specification | Value and source |
|---|---|
| Weights | Open weightsSource |
| Access | LocalSource |
| Parameters | 8BSource |
| Context window | 128,000 tokensSource |
| License | Mistral Research LicenseSource |
| Release | 2024-10-16 (Inventory lists October 15; primary release post is used.)Source |
| Parameters | 8.0198 BSource |
| Architecture | dense transformer, 36 layers, GQA with 8 KV heads, ragged attention (128k,32k,32k,32k)Source |
| Context window | 131,072 tokensSource |
| Input | TextSource |
| Output | TextSource |
| Languages | 10Source |
| Tool calling | YesSource |
Benchmark meta-analysis
These Gradually indexes place selected published measurements on a 0 to 100 scale. They are not accuracy percentages and do not guarantee results in your application.
Not enough independent results for a score yet
provisional
Method version: benchmark-meta-score-v3
The index includes active measurements from reviewed primary sources when at least five models are compared in the same documented comparison slice across dataset, harness, agent, tools, and trial count. Each benchmark series and evaluator contributes one shared setup slice from the newest capture. When one source and series include several explicitly documented reasoning tiers, the highest documented tier (none through max) is selected, never the best result value. The explicit source label “Non-reasoning” is treated as none. Unknown or ambiguous tiers exclude the model from that series. Within each series, percentages and rates are logit-transformed, other units stay unchanged, and all values are then standardized. A one-factor model then estimates one shared strength per model and, per series, how sharply it separates the models. Area scores move away from that strength only as far as the model's own results in the area support. The overall score weights the core areas at 75% and finance, law and health at 25%. It requires at least eight test series from two sources and three areas. The 0 to 100 scale refers to a fixed reference group of 29 language models whose average sits at 50.
Published model-specific parameters can differ. This is not a controlled experiment; comparisons with such differences are marked provisional.
Unmeasured series are not entered as zero. Where a model has no own results in a core area, its overall strength stands in, and the note “estimated without own results in …” names the area. Fewer test series lead to a larger standard error.
The 95% interval follows from the standard error of 200 recalculations with randomly redrawn test families. It describes how precisely the recorded test series determine the value. It is no prediction for future tasks or for later recalculations with other participants.
The source count is not a count of independent experiments.
OECD handbook on composite indicatorsAll 0 test families and 0 of 0 raw measurements are listed here. The complete calculation for every model is available in the JSON export.
Each evaluator measures different capabilities under its own conditions. Test series, composite indices and their components stay separate. The detailed results below retain their scales, configurations and source dates.
No active result from the reviewed external evaluators can currently be attributed to this model. Other source evidence remains available below.
Coverage is not a quality ranking. Overlapping tests and multiple settings do not count as independent experiments. Vendor claims, historical and disputed results are excluded from this overview and remain labelled in the detailed evidence.
These measurements have no matching peer values under the same test conditions. Their original values and sources remain available here.
One SVG prompt and fixed output settings. Captures are shown only with 0 reported reasoning tokens or documented lack of reasoning controls. Identical test conditions do not make the models' internal compute identical.
Unavailable in this round
16.6 / 100
The same official Mistral lifecycle page lists Ministral 8B (API id ministral-8b-2410, released October 2024, matching this directory's ministral-8b profile) as deprecated with a single named recommended replacement, Ministral 3 8B, matching this directory's ministral-3-8b profile (released December 2, 2025).
Mistral AI model lifecycle table (Deprecated models section) (2026-09-19)| Type | Evidence and data date |
|---|---|
| Input | Documented modalities. Retrieved September 8, 2026. |
| Output | Documented modalities. Retrieved September 8, 2026. |
| Research status | Research date September 8, 2026. 3 source URLs checked. This documents the inspected sources, not an exhaustive inventory of every publication. |
| Not found in the inspected sources | knowledge-cutoff Original wording No exact cutoff in checked card/release post. |
| Not found in the inspected sources | training-tokens Original wording No training-token count found. |
| Not found in the inspected sources | max-output-tokens Original wording Card examples use max_tokens but do not declare a model maximum. |
| Unresolved | AA benchmark rows Original wording No exact Artificial Analysis model page located in the checked public sitemap. |
| Additional source | Context window data |
| Additional source | API pricing |
| Additional source | Model metadata (retrieved September 8, 2026) |
| Additional source | Ministral announcement · Editorial description reviewed October 2, 2026 |
| Additional source | Mistral model overview · Editorial description reviewed October 2, 2026 |
| Additional source | OpenRouter: Popularity index (derived), CC BY 4.0 |
| Additional source | Vercel AI Gateway: Popularity index (derived), CC BY 4.0 |
| Additional source | Ollama: cumulative downloads for the popularity index |
| Additional source | Replicate: cumulative runs for the popularity index |
| Additional source | Google Trending Now: limited feed selection for Germany and the US |
| Additional source | Creative Commons Attribution 4.0 |